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⛔️ DEPRECATED brew: Python Ensemble Learning API
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README.rst

[DEPRECATED] brew

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[DEPRECATED] brew: A Multiple Classifier Systems API

This project has not being maintained for a while, so as of now we have abandoned it. If you want an alternative ensemble library in python, we recommend [DESLib](https://github.com/Menelau/DESlib) instead.

This project was started in 2014 by Dayvid Victor and Thyago Porpino
for the Multiple Classifier Systems class at Federal University of Pernambuco.
The aim of this project is to provide an easy API for Ensembling, Stacking,
Blending, Ensemble Generation, Ensemble Pruning, Dynamic Classifier Selection,
and Dynamic Ensemble Selection.

Features

  • General: Ensembling, Stacking and Blending.
  • Ensemble Classifier Generators: Bagging, Random Subspace, SMOTE-Bagging, ICS-Bagging, SMOTE-ICS-Bagging.
  • Dynamic Selection: Overall Local Accuracy (OLA), Local Class Accuracy (LCA), Multiple Classifier Behavior (MCB), K-Nearest Oracles Eliminate (KNORA-E), K-Nearest Oracles Union (KNORA-U), A Priori Dynamic Selection, A Posteriori Dynamic Selection, Dynamic Selection KNN (DSKNN).
  • Ensemble Combination Rules: majority vote, min, max, mean and median.
  • Ensemble Diversity Metrics: Entropy Measure E, Kohavi Wolpert Variance, Q Statistics, Correlation Coefficient p, Disagreement Measure, Agreement Measure, Double Fault Measure.
  • Ensemble Pruning: Ensemble Pruning via Individual Contribution (EPIC).

Example

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import itertools

import sklearn

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier

from brew.base import Ensemble, EnsembleClassifier
from brew.stacking.stacker import EnsembleStack, EnsembleStackClassifier
from brew.combination.combiner import Combiner

from mlxtend.data import iris_data
from mlxtend.evaluate import plot_decision_regions

# Initializing Classifiers
clf1 = LogisticRegression(random_state=0)
clf2 = RandomForestClassifier(random_state=0)
clf3 = SVC(random_state=0, probability=True)

# Creating Ensemble
ensemble = Ensemble([clf1, clf2, clf3])
eclf = EnsembleClassifier(ensemble=ensemble, combiner=Combiner('mean'))

# Creating Stacking
layer_1 = Ensemble([clf1, clf2, clf3])
layer_2 = Ensemble([sklearn.clone(clf1)])

stack = EnsembleStack(cv=3)

stack.add_layer(layer_1)
stack.add_layer(layer_2)

sclf = EnsembleStackClassifier(stack)

clf_list = [clf1, clf2, clf3, eclf, sclf]
lbl_list = ['Logistic Regression', 'Random Forest', 'RBF kernel SVM', 'Ensemble', 'Stacking']

# Loading some example data
X, y = iris_data()
X = X[:,[0, 2]]

# WARNING, WARNING, WARNING
# brew requires classes from 0 to N, no skipping allowed
d = {yi : i for i, yi in enumerate(set(y))}
y = np.array([d[yi] for yi in y])

# Plotting Decision Regions
gs = gridspec.GridSpec(2, 3)
fig = plt.figure(figsize=(10, 8))

itt = itertools.product([0, 1, 2], repeat=2)

for clf, lab, grd in zip(clf_list, lbl_list, itt):
    clf.fit(X, y)
    ax = plt.subplot(gs[grd[0], grd[1]])
    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
    plt.title(lab)
plt.show()

decision regions plots

Dependencies

  • Python 2.7+
  • scikit-learn >= 0.15.2
  • Numpy >= 1.6.1
  • SciPy >= 0.9
  • Matplotlib >= 0.99.1 (examples, only)
  • mlxtend (examples, only)

Installing

You can easily install brew using pip:

pip install brew

or, if you prefer an up-to-date version, get it from here:

pip install git+https://github.com/viisar/brew.git

Important References

  • Kuncheva, Ludmila I. Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2014.
  • Zhou, Zhi-Hua. Ensemble methods: foundations and algorithms. CRC Press, 2012.